3D Model Reconstruction Using Statistical Shape Prior
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Solution Overview
Problem
Current 3D object reconstruction methods for mobile devices are inefficient and lack accuracy, particularly when requiring high precision for secure digital transactions, as they necessitate numerous iterations and uncertain data quality.
Innovation Solution
A method involving the detection of object positions in multiple image frames, generation of depth maps, fusion of these maps to create a 3D model, calculation of variance for quality assessment, and confirmation of accuracy based on a predetermined threshold, utilizing a morphable face model as a statistical shape prior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing 3D reconstruction methods are used to achieve high accuracy, then manufacturing precision is improved, but productivity deteriorates due to significant number of iterations required
Solution Approach 1:
The patent applies preliminary action by using a statistical shape prior (morphable face model) to pre-establish a framework of expected facial geometries and variations. This prior knowledge guides the reconstruction process, allowing the system to converge to accurate results with fewer iterations rather than starting from scratch each time.
Solution Approach 2:
The patent implements feedback through quality monitoring mechanisms that evaluate the fitted 3D model against the statistical shape prior during the reconstruction process. This feedback loop allows the system to assess convergence quality and determine when sufficient accuracy is achieved, preventing unnecessary additional iterations.
2Manufacturing precision
If existing 3D reconstruction methods are used to achieve high accuracy, then manufacturing precision is improved, but loss of time increases due to multiple iterations
Solution Approach 1:
The statistical shape prior pre-computes and stores common facial shape variations, allowing the reconstruction algorithm to quickly find the best matching template rather than computing all possibilities in real-time, thus reducing reconstruction time while maintaining accuracy.
Solution Approach 2:
The quality monitoring mechanism enables the system to skip unnecessary iterations by detecting when the reconstruction has achieved sufficient accuracy. Once the fitted model satisfies the quality threshold against the statistical prior, the process terminates early, avoiding time-consuming additional iterations.
3Reliability
If quality monitoring is added to ensure accuracy, then reliability is improved, but device complexity increases
Solution Approach 1:
The quality monitoring system provides feedback by comparing the reconstructed 3D model against the statistical shape prior and computing a quality metric. This feedback mechanism ensures reliability by confirming that the reconstruction meets minimum quality thresholds before accepting the result for identity verification.
Solution Approach 2:
The statistical shape prior acts as an intermediary between the raw depth map data and the final 3D model. It mediates the reconstruction process by providing a reference framework that simplifies the complexity of directly interpreting raw sensor data, making the quality assessment more manageable and reliable.
Data Source
AI summary
A system and method is provided for accurately reconstructing a three-dimensional object using depth maps. An exemplary method includes detecting different positions of an object in a multiple image frames and generating depth maps for the object from the images frames based on the detected different positions of the object. Moreover, the method includes fusing the generated depth maps to generate a three-dimensional model of the object, calculating a variance of points of the fused depth maps for the object, and obtaining respective variances of points of a statistical value prior that correspond to the points of the fused depth maps. Finally, the method calculates a quality fitting result of the generated three-dimensional model of the object based on the calculated variance of the points of the fused depth maps and the respective variances of the corresponding points of the statistical value prior.